Machine Learning Fundamentals
Master machine learning with project-based learning on real-world applications of neural networks, data modeling, and predictive analytics.
Course Overview
- Hands-on projects using Python, TensorFlow, and Scikit-learn
- Supervised and unsupervised learning techniques
- Real-world AI case studies from healthcare, finance, and cybersecurity
What You'll Learn
- → Build and deploy machine learning models
- → Clean and interpret complex datasets
- → Design deep learning architectures
- → Apply ML in fraud detection, recommendation systems, and NLP
12-Week Program | 40+ Hours
Course Curriculum
Week 1-3: Foundations
- Math review (linear algebra, calculus)
- Python fundamentals & Jupyter setup
- Intro to ML concepts and algorithms
Week 4-6: Core Techniques
- Supervised learning algorithms
- Unsupervised learning methods
- Model evaluation and bias detection
Week 7-9: Advanced Concepts
- Deep learning with neural networks
- Computer vision and NLP applications
- ML ethics and responsible AI
Week 10-12
- Capstone projects
- Industry use case analysis
- Deployment and monitoring
Your Instructor
Dr. Sarah Lin
AI & Machine Learning Expert
With 15+ years in AI research, Dr. Lin leads our machine learning curriculum. She has led ML projects at top tech firms and published 50+ papers on ethical AI.
Stanford AI Lab Alum
15+ Years Experience
Ready to Start Learning?
Join our next cohort and transform how you approach data science and machine learning.